Freeprotmap: waiting-free prediction method for protein distance map.

BACKGROUND: Protein residue-residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue-residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed. RESULT: In this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue-residue distances in tens of milliseconds and has higher precision than the best structure prediction method. CONCLUSION: Several groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue-residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.

Advancements in clinical approaches, analytical methods, and smart sampling for LC-MS-based protein determination from dried matrix spots.

Determination of proteins from dried matrix spots using MS is an expanding research area. Mainly, the collected dried matrix sample is whole blood from a finger or heal prick, resulting in dried blood spots. However as other matrices such as plasma, serum, urine, and tear fluid also can be collected in this way, the term dried matrix spot is used as an overarching term. In this review, the focus is on advancements in the field made from 2017 up to 2023. In the first part reviews concerning the subject are discussed. After this, advancements made for clinical purposes are highlighted. Both targeted protein analyses, with and without the use of affinity extractions, as well as untargeted, global proteomic approaches are discussed. In the last part, both methodological advancements are being reviewed as well as the possibility to integrate sample preparation steps during the sample handling. The focus, of this so-called smart sampling, is on the incorporation of cell separation, proteolysis, and antibody-based affinity capture.

Harnessing the 3D-Beacons Network: A Comprehensive Guide to Accessing and Displaying Protein Structure Data.

Recent advancements in protein structure determination and especially in protein structure prediction techniques have led to the availability of vast amounts of macromolecular structures. However, the accessibility and integration of these structures into scientific workflows are hindered by the lack of standardization among publicly available data resources. To address this issue, we introduced the 3D-Beacons Network, a unified platform that aims to establish a standardized framework for accessing and displaying protein structure data. In this article, we highlight the importance of standardized approaches for accessing protein structure data and showcase the capabilities of 3D-Beacons. We describe four protocols for finding and accessing macromolecular structures from various specialist data resources via 3D-Beacons. First, we describe three scenarios for programmatically accessing and retrieving data using the 3D-Beacons API. Next, we show how to perform sequence-based searches to find structures from model providers. Then, we demonstrate how to search for structures and fetch them directly into a workflow using JalView. Finally, we outline the process of facilitating access to data from providers interested in contributing their structures to the 3D-Beacons Network. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Programmatic access to the 3D-Beacons API Basic Protocol 2: Sequence-based search using the 3D-Beacons API Basic Protocol 3: Accessing macromolecules from 3D-Beacons with JalView Basic Protocol 4: Enhancing data accessibility through 3D-Beacons.

Anti-Drug Antibody Incidence Comparison of Therapeutic Proteins Administered Via Subcutaneous vs. Intravenous Route.

Subcutaneous (SC) administration of therapeutic proteins is perceived to pose higher risk of immunogenicity when compared with intravenous (IV) route of administration (RoA). However, systematic evaluations of clinical data to support this claim are lacking. This meta-analysis was conducted to compare the immunogenicity of the same therapeutic protein by IV and SC RoA. Anti-drug antibody (ADA) data and controlling variables for 7 therapeutic proteins administered by both IV and SC routes across 48 treatment groups were analyzed. RoA was the primary independent variable of interest while therapeutic protein, patient population, adjusted dose, and number of ADA samples were controlling variables. Analysis of variance was used to compare the ADA incidence between IV and SC RoA, while accounting for controlling variables and potential interactions. Subsequently, 10 additional therapeutic proteins with ADA data published for both IV and SC administration were added to the above 7 therapeutic proteins and were evaluated for ADA incidence. RoA had no statistically significant effect on ADA incidence for the initial dataset of 7 therapeutic proteins (p = 0.55). The only variable with a significant effect on ADA incidence was the therapeutic protein. None of the other controlling variables, including their interactions with RoA, was significant. When all data from the 17 therapeutic proteins were pooled, there was no statistically significant effect of RoA on ADA incidence (p = 0.81). In conclusion, there is no significant difference in ADA incidence between the IV and SC RoA, based on analysis of clinical ADA data from 17 therapeutic proteins.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics.

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Structure prediction of protein-ligand complexes from sequence information with Umol.

Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information. We find that classical docking methods are still superior, but depend upon having crystal structures of the target protein. In addition to predicting flexible all-atom structures, predicted confidence metrics (plDDT) can be used to select accurate predictions as well as to distinguish between strong and weak binders. The advances presented here suggest that the goal of AI-based drug discovery is one step closer, but there is still a way to go to grasp the complexity of protein-ligand interactions fully. Umol is available at: https://github.com/patrickbryant1/Umol .

A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions.

Protein functions are characterized by interactions with proteins, drugs, and other biomolecules. Understanding these interactions is essential for deciphering the molecular mechanisms underlying biological processes and developing new therapeutic strategies. Current computational methods mostly predict interactions based on either molecular network or structural information, without integrating them within a unified multi-scale framework. While a few multi-view learning methods are devoted to fusing the multi-scale information, these methods tend to rely intensively on a single scale and under-fitting the others, likely attributed to the imbalanced nature and inherent greediness of multi-scale learning. To alleviate the optimization imbalance, we present MUSE, a multi-scale representation learning framework based on a variant expectation maximization to optimize different scales in an alternating procedure over multiple iterations. This strategy efficiently fuses multi-scale information between atomic structure and molecular network scale through mutual supervision and iterative optimization. MUSE outperforms the current state-of-the-art models not only in molecular interaction (protein-protein, drug-protein, and drug-drug) tasks but also in protein interface prediction at the atomic structure scale. More importantly, the multi-scale learning framework shows potential for extension to other scales of computational drug discovery.

Quantitative entropy-enthalpy compensation in intraprotein interactions from model compound data.

Many small globular proteins exist in only two states-the physiologically relevant folded state and an inactive unfolded state. The active state is stabilized by numerous weak attractive contacts, including hydrogen bonds, other polar interactions, and the hydrophobic effect. Knowledge of these interactions is key to understanding the fundamental equilibrium thermodynamics of protein folding and stability. We focus on one such interaction, that between amide and aromatic groups. We provide a statistically convincing case for quantitative, linear entropy-enthalpy compensation in forming aromatic-amide interactions using published model compound transfer-free energy data.

Diruthenium Paddlewheel Complexes Attacking Proteins: Axial versus Equatorial Coordination.

Metallodrugs are an important group of medicinal agents used for the treatment of various diseases ranging from cancers to viral, bacterial, and parasitic diseases. Their distinctive features include the availability of a metal centre, redox activity, as well as the ability to multitarget. Diruthenium paddlewheel complexes are an intensely developing group of metal scaffolds, which can securely coordinate bidentate xenobiotics and transport them to target tissues, releasing them by means of substitution reactions with biomolecular nucleophiles. It is of the utmost importance to gain a complete comprehension of which chemical reactions happen with them in physiological milieu to design novel drugs based on these bimetallic scaffolds. This review presents the data obtained in experiments and calculations, which clarify the chemistry these complexes undergo once administered in the proteic environment. This study demonstrates how diruthenium paddlewheel complexes may indeed embody a new paradigm in the design of metal-based drugs of dual-action by presenting and discussing the protein metalation by these complexes.

A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models.

The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies, data and feature construction. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.

BET inhibition reforms the immune microenvironment and alleviates T cell dysfunction in chronic lymphocytic leukemia.

Redundant tumor microenvironment (TME) immunosuppressive mechanisms and epigenetic maintenance of terminal T cell exhaustion greatly hinder functional antitumor immune responses in chronic lymphocytic leukemia (CLL). Bromodomain and extraterminal (BET) proteins regulate key pathways contributing to CLL pathogenesis and TME interactions, including T cell function and differentiation. Herein, we report that blocking BET protein function alleviates immunosuppressive networks in the CLL TME and repairs inherent CLL T cell defects. The pan-BET inhibitor OPN-51107 reduced exhaustion-associated cell signatures resulting in improved T cell proliferation and effector function in the Eµ-TCL1 splenic TME. Following BET inhibition (BET-i), TME T cells coexpressed significantly fewer inhibitory receptors (IRs) (e.g., PD-1, CD160, CD244, LAG3, VISTA). Complementary results were witnessed in primary CLL cultures, wherein OPN-51107 exerted proinflammatory effects on T cells, regardless of leukemic cell burden. BET-i additionally promotes a progenitor T cell phenotype through reduced expression of transcription factors that maintain terminal differentiation and increased expression of TCF-1, at least in part through altered chromatin accessibility. Moreover, direct T cell effects of BET-i were unmatched by common targeted therapies in CLL. This study demonstrates the immunomodulatory action of BET-i on CLL T cells and supports the inclusion of BET inhibitors in the management of CLL to alleviate terminal T cell dysfunction and potentially enhance tumoricidal T cell activity.

On the role of native contact cooperativity in protein folding.

The consistency of energy landscape theory predictions with available experimental data, as well as direct evidence from molecular simulations, have shown that protein folding mechanisms are largely determined by the contacts present in the native structure. As expected, native contacts are generally energetically favorable. However, there are usually at least as many energetically favorable nonnative pairs owing to the greater number of possible nonnative interactions. This apparent frustration must therefore be reduced by the greater cooperativity of native interactions. In this work, we analyze the statistics of contacts in the unbiased all-atom folding trajectories obtained by Shaw and coworkers, focusing on the unfolded state. By computing mutual cooperativities between contacts formed in the unfolded state, we show that native contacts form the most cooperative pairs, while cooperativities among nonnative or between native and nonnative contacts are typically much less favorable or even anticooperative. Furthermore, we show that the largest network of cooperative interactions observed in the unfolded state consists mainly of native contacts, suggesting that this set of mutually reinforcing interactions has evolved to stabilize the native state.

[Advances in the application of AlphaFold2: a protein structure prediction model].

Protein structure prediction is an important research field in life sciences and medicine, and it is also a key application scenario of artificial intelligence in scientific research. AlphaFold2 is a protein structure prediction system developed by DeepMind based on deep learning, capable of efficiently generating the atomic-scale spatial structure of a protein from the amino acid sequence. It has demonstrated superior performance in the prediction of protein structures since its inception, thus attracting much attention and research. This paper introduces the model architecture, highlights, limitations, and application progress of AlphaFold2. Furthermore, it briefs the capabilities, highlights, and limitations of several other types of protein structure prediction models and prospects the future development direction in this field.

Japanese encephalitis virus hijacks ER-associated degradation regulators for its replication.

Flaviviruses target their replication on membranous structures derived from the ER, where both viral and host proteins play crucial structural and functional roles. Here, we have characterized the involvement of the ER-associated degradation (ERAD) pathway core E3 ligase complex (SEL1L-HRD1) regulator proteins in the replication of Japanese encephalitis virus (JEV). Through high-resolution immunofluorescence imaging of JEV-infected HeLa cells, we observe that the virus replication complexes marked by NS1 strongly colocalize with the ERAD adapter SEL1L, lectin OS9, ER-membrane shuttle factor HERPUD1, E3 ubiquitin ligase HRD1 and rhomboid superfamily member DERLIN1. NS5 positive structures also show strong overlap with SEL1L. While these effectors show significant transcriptional upregulation, their protein levels remain largely stable in infected cells. siRNA mediated depletion of OS9, SEL1L, HERPUD1 and HRD1 significantly inhibit viral RNA replication and titres, with SEL1L depletion showing the maximum attenuation of replication. By performing protein translation arrest experiments, we show that SEL1L, and OS9 are stabilised upon JEV infection. Overall results from this study suggest that these ERAD effector proteins are crucial host-factors for JEV replication.

Genetic Encoding of Phosphorylated Amino Acids into Proteins.

Reversible phosphorylation is a fundamental mechanism for controlling protein function. Despite the critical roles phosphorylated proteins play in physiology and disease, our ability to study individual phospho-proteoforms has been hindered by a lack of versatile methods to efficiently generate homogeneous proteins with site-specific phosphoamino acids or with functional mimics that are resistant to phosphatases. Genetic code expansion (GCE) is emerging as a transformative approach to tackle this challenge, allowing direct incorporation of phosphoamino acids into proteins during translation in response to amber stop codons. This genetic programming of phospho-protein synthesis eliminates the reliance on kinase-based or chemical semisynthesis approaches, making it broadly applicable to diverse phospho-proteoforms. In this comprehensive review, we provide a brief introduction to GCE and trace the development of existing GCE technologies for installing phosphoserine, phosphothreonine, phosphotyrosine, and their mimics, discussing both their advantages as well as their limitations. While some of the technologies are still early in their development, others are already robust enough to greatly expand the range of biologically relevant questions that can be addressed. We highlight new discoveries enabled by these GCE approaches, provide practical considerations for the application of technologies by non-GCE experts, and also identify avenues ripe for further development.

Hybrid Small-Molecule/Protein Fluorescent Probes.

Hybrid small-molecule/protein fluorescent probes are powerful tools for visualizing protein localization and function in living cells. These hybrid probes are constructed by diverse site-specific chemical protein labeling approaches through chemical reactions to exogenous peptide/small protein tags, enzymatic post-translational modifications, bioorthogonal reactions for genetically incorporated unnatural amino acids, and ligand-directed chemical reactions. The hybrid small-molecule/protein fluorescent probes are employed for imaging protein trafficking, conformational changes, and bioanalytes surrounding proteins. In addition, fluorescent hybrid probes facilitate visualization of protein dynamics at the single-molecule level and the defined structure with super-resolution imaging. In this review, we discuss development and the bioimaging applications of fluorescent probes based on small-molecule/protein hybrids.

Unnatural Amino Acids for Biological Spectroscopy and Microscopy.

Due to advances in methods for site-specific incorporation of unnatural amino acids (UAAs) into proteins, a large number of UAAs with tailored chemical and/or physical properties have been developed and used in a wide array of biological applications. In particular, UAAs with specific spectroscopic characteristics can be used as external reporters to produce additional signals, hence increasing the information content obtainable in protein spectroscopic and/or imaging measurements. In this Review, we summarize the progress in the past two decades in the development of such UAAs and their applications in biological spectroscopy and microscopy, with a focus on UAAs that can be used as site-specific vibrational, fluorescence, electron paramagnetic resonance (EPR), or nuclear magnetic resonance (NMR) probes. Wherever applicable, we also discuss future directions.